Swearing and Religion in American Rap

Loading data and packages

As a case study, let’s analyze and compare a few famous American Rappers in the 21st Century.

The datasets above were obtained by running the script “Scrapping_American Rappers.R”for each singer. After loading the packages and datasets, we need to join these together. If we simply join the datasets though, we will lose the name of the artist. So let’s add a name variable to each dataset before joining them.

Eminem$rapper <- "Eminem"
Kanye_West$rapper <- "Kanye West"
Jay_Z$rapper <- "Jay Z"
Kendrick_Lamar$rapper <- "Kendrick Lamar"
Queen_latifah$rapper <- "Queen Latifah"
Cardi_B$rapper <- "Cardi B"
Nicki_Minaj$rapper <- "Nicki Minaj"

# # How could you do this in tidy?
# Eminem <- Eminem %>% mutate(rapper= "Eminem")

Joining data

Now let’s join data! One option is to embedded a series of full_joins inside full_joins.

american_rappers <- full_join(Eminem,
                              full_join(Kanye_West,
                                        full_join(Jay_Z,
                                                  full_join(Nicki_Minaj,
                                                                   full_join(Cardi_B, full_join(Queen_latifah,Kendrick_Lamar))))))

We can only join them without specifying the “by =” argument because the datasets have the same variables with the same names. However, this is not a very elegant solution. A more elegant solution for joining multiple datasets would entail using the ´{purrr}´ package. You can learn more about purrr here.

library(purrr)
american_rappers <- list(Eminem, Kanye_West, Jay_Z, Kendrick_Lamar,Nicki_Minaj, Cardi_B, Queen_latifah) %>%
  reduce(full_join)

There are probably more efficient solutions than this one, but let’s stop here.

Data cleaning and wrangling

Okay, let’s clean and wrangle the data before we can get to the analysis! Open it up and see how messy and dirty it is. What do we need to do?

First, there are some songs in the dataset that do not come from rappers’ albums, but from somewhere else. In the album variables in the dataset, songs that come from an album from one of the rappers start with “album:”.

american_rappers$title <- gsub('"', "", american_rappers$title) # getting read of the titles' quotation marks...

american_rappers$album <- ifelse(startsWith(american_rappers$album, "album:"),
                                 gsub("album:", "", american_rappers$album),
                                 NA_character_)

# # The same with tidy:
# american_rappers <- american_rappers %>%
#   mutate(album = ifelse(stringr::str_detect(album, "album:"),
#                         stringr::str_replace(album, "album:", ""),
#                         NA_character_))

Second, we can also extract the date from the album variable to create an 4 digit year variable.The package stringr from tidyverse can help us here! Check it’s nice cheat sheet here.

american_rappers$year <- as.numeric(stringr::str_extract_all(american_rappers$album,
                                                             "[:digit:]{4}"))

Third, let’s remove songs for which we are missing the album or that come from album collaborations.

american_rappers <- na.omit(american_rappers)

Fourth, let’s clean the text by removing signs, transform to lower case, and more. The package tm has some nice functions for this.

american_rappers$lyrics <- tm::removePunctuation(american_rappers$lyrics) #removing punctuation

american_rappers$lyrics <- tolower(american_rappers$lyrics) #making all lowercase

american_rappers$lyrics <- gsub("\r|\n", " ", american_rappers$lyrics) # remove line markers 

american_rappers$title <- tm::removePunctuation(american_rappers$title) # remove punctuation

american_rappers$album <- stringr::str_remove_all(american_rappers$album,
                                                  "\\([:digit:]{4}\\)") # remove the years from albums

Dictionary and Counting

Create a dictionary for religion and swearing: can you help? Kidding.

swear_words <- "fuck|bitch|pussy|shit|dick|ass|cunt"
religious_words <- "god|bible|jesus|hell|heaven|lord|praise"

# base for counting swear words
american_rappers$swear_words <- stringr::str_count(american_rappers$lyrics,
                                                   swear_words)
# tidy for religion
american_rappers <- american_rappers %>%
  mutate(religious_words = stringr::str_count(american_rappers$lyrics, 
                                              religious_words))

Swearing in American Rappers’ songs

ggplot(american_rappers, aes(year, swear_words)) +
  geom_point(aes(size = swear_words), color="gold") +
  geom_text(aes(label = title), check_overlap=T, size=3) +
  labs(x = "", y = "Count",
       title = "Swearing in American Rappers' songs",
       subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey",
                                        size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none")

What are some problems with this plot? How can we deal with outliers? One way is go check them on your own and then decide, based on the purpose of the analysis/visualization, whether to remove them or keep them.

american_rappers %>% filter(swear_words < 100) %>%
  ggplot(., aes(year, swear_words)) +
  geom_point(aes(color=rapper))+
  geom_text(aes(label = title), check_overlap=T, size=3, face="bold") +
  scale_color_brewer(palette="Set2")+
  labs(x = "", y = "Count",
       title = "Swearing in American Rappers' songs",
       subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

american_rappers %>% filter(swear_words < 100) %>%
  ggplot(., aes(year, swear_words)) +
  geom_text(aes(label = title, color=rapper), check_overlap=T, size=3.5, face="bold") +
  scale_color_brewer(palette="Set2")+
  labs(x = "", y = "Count",
       title = "Swearing in American Rappers' songs",
       subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

# Want to see swearing by albums?
american_rappers %>% 
  group_by (album, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE))%>%
  ggplot(., aes(y=swear_words, x=album)) +
  geom_bar(aes(fill=rapper), stat="identity") +
  scale_fill_brewer(palette="Set2")+
  labs(y = "Count", x = "Album Title",
       title = "Swearing in American Rappers' Albums",
       subtitle= "61 Albums since 1996")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

This looks ugly.. How can we improve it a bit?

# Want to see swearing by albums?
american_rappers %>% 
  group_by (album, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE))%>%
  ggplot(., aes(x=swear_words, y=reorder(album, swear_words))) +
  geom_bar(aes(fill=rapper), stat="identity") +
  scale_fill_brewer(palette="Set2")+
  labs(x = "Count", y = "Album Title",
       title = "Swearing in American Rappers' Albums",
       subtitle= "61 Albums since 1996")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

Has swearing increased in time?

american_rappers %>% 
  group_by (year, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE))%>%
  ggplot(., aes(year, swear_words)) +
  geom_smooth(se=FALSE, color="black") +
  labs(x = "Year", y = "Swear Words Count",
       title = "Swear words by year",
       subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none")

What are the problems with this plot? The dataset is unbalanced, meaning that descriptive trends might reflect different sample compositions along time whether than real patterns in the data:

american_rappers %>% 
  group_by (year, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE))%>%
  ggplot(., aes(year, swear_words)) +
  geom_smooth(se=FALSE, color="black") +
  labs(x = "Year", y = "Swear Words Count",
       title = "Unbalanced dataset: only three rappers rapping in the 1990s.",
       subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none") +
  facet_wrap(~rapper)

What are some other issue is with this analysis?

Yes, we need to normalize scores!

But what would be the best normalization given our data?

american_rappers %>%
  group_by(year, rapper) %>%
  mutate(songs_per_year = n())%>%
  ungroup()%>%
  group_by(year,songs_per_year, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE)) %>%
  mutate(normalized_swear_words = swear_words/songs_per_year) %>% 
  #all lines until here are normalizing by songs per year
  ggplot(., aes(swear_words, songs_per_year)) +
  geom_jitter(alpha=.75, color="tomato", size=2.5)+
  geom_smooth(se=FALSE, color="grey") +
  labs(x = "Number of Swear words", y = "Number of Songs",
       title ="Yearly relationship between number of songs and swear words",
              subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none")

american_rappers %>%
  group_by(year, rapper) %>%
  mutate(songs_per_year = n()) %>%
  group_by(year,songs_per_year, rapper) %>%
  summarise(swear_words = sum(swear_words, na.rm = TRUE)) %>%
  mutate(normalized_swear_words = swear_words/songs_per_year)%>% 
  #all lines until here are normalizing by songs per year
  ggplot(., aes(year, normalized_swear_words)) +
  geom_smooth(se=FALSE, color="black") +
  labs(x = "", y = "Normalized count by songs per year",
       title = " Average swearing per song by year",
              subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none") +
  facet_wrap(~rapper)

Let’s actually normalize by the number of words per song

american_rappers$word_per_song <-str_count(american_rappers$lyrics, "\\w+")
# counting words in lyrics
american_rappers$Swearing <- american_rappers$swear_words /
  american_rappers$word_per_song # normalize for swear words
american_rappers$Preaching <- american_rappers$religious_words / 
  american_rappers$word_per_song # normalize for religious words
# Plot
american_rappers %>%
  ggplot(., aes(year, Swearing)) +
  geom_smooth(se=FALSE, color="black") +
  scale_y_continuous(labels = scales::percent_format())+ # the scales package helps you with making scales more beautiful!
  labs(x = "", y = "Swearing per Song",
       title = "Average number of swear words per song",
              subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none") +
  facet_wrap(~rapper)

Religious vocabulary in time: smoothing is okay?

american_rappers %>%
  group_by(year) %>%
  summarise(Swearing=mean(Swearing),
            Preaching=mean(Preaching)) %>%
    pivot_longer(2:3, names_to="topic", values_to="normalized_count2")%>%
  ggplot(., aes(x=year, y=normalized_count2, color=topic)) +
  geomtextpath::geom_textsmooth(aes(label=topic), #in line legends are nice sometimes, just remember to specify the label argument
    se=FALSE, size=3.5)+ 
  scale_color_brewer(palette="Set2")+
  scale_y_continuous(labels = scales::percent_format())+ 
  labs(x = "", y = "",
       title = " Religion and Swearing in American Rappers' Repertoire",
              subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none")

The plot above feels smooth… Why is it so smooth?

american_rappers %>%
  group_by(year) %>%
  summarise(Swearing=mean(Swearing),
            Preaching=mean(Preaching)) %>%
    pivot_longer(2:3, names_to="topic", values_to="normalized_count2") %>%
  ggplot(., aes(x=year, y=normalized_count2, color=topic)) +
  geom_line(size=1)+
    scale_color_brewer(palette="Set2")+
    scale_y_continuous(labels = scales::percent_format())+ 
  labs(x = "", y = "",
       title = " Religion and Swearing in American Rappers' Repertoire",
              subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

Do you think there is a trend a wider trend in American Rappers using more religious words in songs?

american_rappers <- american_rappers %>%
  mutate(gender=case_when(rapper == "Cardi B" ~ "Female",
                          rapper =="Nicki Minaj" ~ "Female",
                          rapper == "Queen Latifah" ~"Female", TRUE ~ "Male"),
         two_thousands=ifelse(year>=2000, "Before 2000s","After 2000s"),
         swearing = scale(Swearing),
         preaching = scale(Preaching)) #just normalizing the measures to facilitate model interpretation

m1 <- lm(swearing ~ preaching,
                      data = american_rappers) #simple linear model with one i.V. and one d.V.

m2 <- lm(swearing ~ preaching + as.factor(gender) + 
            as.factor(two_thousands),
                      data = american_rappers) # adding a few controls.

m3 <- lm(swearing ~ preaching + as.factor(gender) + 
            as.factor(two_thousands)+ as.factor(rapper),
                      data = american_rappers) 

 
m4 <- plm(Swearing ~ Preaching + as.factor(gender) + as.factor(two_thousands),
          data = american_rappers, model = "within", index = "rapper") #this one is a fixed effect model indexed by rapper

# a nice way to quickly check your model is tab_model() from the package sJPlot
tab_model(m4)

	Swearing
Predictors	Estimates	CI	p
Preaching	-0.09	-0.16 – -0.01	0.023
two thousands [Before 2000s]	-0.00	-0.01 – 0.00	0.327
Observations	989
R2 / R2 adjusted	0.006 / -0.002

Some people say tables should always be figures (Kastellec and Leoni 2007)… Let’s plot the model:

plot_models(m3,m2,m1, 
            grid=TRUE, 
            ci.lvl = .99, 
            m.labels=c("Model 3", "Model 2", "Model 1"),
            axis.labels = c("Rapper(Queen Latifah)","Rapper(Nicki Minaj)",
                            "Rapper(Kayne West)",
                            "Rapper(Jay Z)","Rapper(Eminem)",
                            "Period(b4 2000s)", "Gender(male)",
                            "Preaching"),
            show.p = TRUE)+
    geom_hline(yintercept = 0,linetype="dotted")+
    ylim(-2, 0.5)+
  ggtitle("Three simple linear models predicting swearing")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "none")

Not that good of a model, right?

american_rappers %>%
  pivot_longer(9:10, names_to= "topic", values_to="normalized_count2") %>%
  ggplot(., aes(x=year, y=normalized_count2, color=topic)) +
  geom_smooth(se=FALSE) +
    scale_color_brewer(palette="Set2")+
    scale_y_continuous(labels = scales::percent_format())+ 
  labs(x = "", y = "",
       title = " Religion and Swearing in American Rappers' Repertoire",
        subtitle= "989 songs by seven American Rappers since 1990")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom") +
  facet_wrap(~rapper)

Is there really a wider trend or this is all driven by Kanye?

The Kardashian effect

Is there a Kardashian effect in this switch we see for Kayne?

Kanye and Kim were a couple from 2011 to 2020, let’s create a Kim variable!

Kanye_West <- american_rappers %>% 
  filter(rapper== "Kanye West")%>%
  mutate(kim_kardashian= case_when(year > 2010 & year <= 2020  ~ "With Kim",
                                   year < 2011  ~ "Before Kim",
                                   year >2019  ~ "After Kim"))
Kanye_West %>%
  gather("topic", "word_count", 6:7) %>%
  group_by(year) %>%
  mutate(songs_per_year = n()) %>%
  group_by(songs_per_year, topic, kim_kardashian) %>%
  summarise(word_count = sum(word_count, na.rm = TRUE))%>%
  mutate(normalized_word_count = word_count/songs_per_year)%>%
  ggplot(., aes(x=kim_kardashian, y=normalized_word_count, fill=topic)) +
  geom_bar(stat="identity", position="dodge") +
  labs(x = "", y = "Average swearing per song",
       title = " The Kardashian Effect?",
       subtitle= "214 songs in 13 albums since 2004.")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

Kanye_West$kim_kardashian <- factor(Kanye_West$kim_kardashian,                      
                                    levels = c("Before Kim",
                                               "With Kim",
                                               "After Kim")) # reorder and run it again!!
Kanye_West %>%
  gather("topic", "word_count", 6:7) %>%
  group_by(year) %>%
  mutate(songs_per_year = n()) %>%
  group_by(songs_per_year, topic, kim_kardashian) %>%
  summarise(word_count = sum(word_count, na.rm = TRUE))%>%
  mutate(normalized_word_count = word_count/songs_per_year)%>%
  ggplot(., aes(x=kim_kardashian, y=normalized_word_count, fill=topic)) +
  geom_bar(stat="identity", position="dodge") +
  labs(x = "", y = "Average swearing per song",
       title = " The Kardashian Effect?",
       subtitle= "214 songs in 13 albums since 2004.")+
  theme(panel.background = element_rect("white", "black", .5, "solid"),
        panel.grid.major = element_line(color = "grey", size = 0.3,
                                        linetype = "solid"),
        axis.text = element_text(color = "black", size = 10),
        title = element_text(color = "black", size = 10, face = "bold"),
        legend.title = element_blank(),
        plot.subtitle = element_text(color = "black", size = 9, face = "plain"),
        legend.position = "bottom")

Lamar’s Repertoire in words

Let’s do a basic word frequency analysis.

# The function "function()" creates a function; 
# in this case, our function cleans the text in multiple ways, 
# by applying already existing functions from tm.

cleanCorpus<-function(corpus, customStopwords){
  corpus <- tm_map(corpus, content_transformer(qdapRegex::rm_url))
  corpus <- tm_map(corpus, removeNumbers)
  corpus <- tm_map(corpus, removePunctuation)
  corpus <- tm_map(corpus, stripWhitespace)
  corpus <- tm_map(corpus, removeWords, customStopwords)
  return(corpus)
}

stops <- c(stopwords('SMART'), "dont", "youre")
# stopwords ('SMART') is a vector with common words (a, the, about...) that we want to remove from the songs

Kendrick_Lamar <- american_rappers %>%
  filter(rapper=="Kendrick Lamar") # creating a dataset only for Kendrick
  
# The next few lines perform a few operations to create a dataset counting appearances of words in all songs.
lamarCorpus_t <- VCorpus(VectorSource(Kendrick_Lamar$lyrics))
lamarCorpus_t <- cleanCorpus(lamarCorpus_t, stops)
LamarTDM_t  <- TermDocumentMatrix(lamarCorpus_t)
lamar_dtm_t  <- DocumentTermMatrix(lamarCorpus_t)

# create a document term matrix
LamarTDMm_t <- as.matrix(LamarTDM_t)
LamarSums_t <- rowSums(LamarTDMm_t)
LamarFreq_t <- data.frame(word=names(LamarSums_t),frequency=LamarSums_t)
rownames(LamarFreq_t) <- NULL
topWords_t  <- subset(LamarFreq_t, LamarFreq_t$frequency >= 50)
#here we are subsetting to those that appear more than 50 times.

topWords_t  <- topWords_t[order(topWords_t$frequency, decreasing=F),]
topWords_t$word <- factor(topWords_t$word, levels=unique(as.character(topWords_t$word)))
# getting the top words in frequency

ggplot(topWords_t, aes(x=word, y=log(frequency))) +
  geom_bar(stat="identity", fill='gold') +
  coord_flip()+
  geom_text(aes(label=frequency), colour="black",hjust=1.25, size=3.0)+
  labs( x="", y= "", title= "Most frequent words in Lamar's repertoire",
        subtitle="All albums since 2011")+
  theme(panel.background = element_rect ("white", "black", .5, "solid"),
        panel.grid.major = element_line(color="grey", size=0.3, linetype= "solid"),
        axis.text = element_text(color="black", size=10),
        title = element_text(color="black", size=10, face="bold"),
        axis.text.x=element_blank(),
        plot.subtitle = element_text(color="black", size=9, face= "plain"),
        legend.position = "bottom")

Is our sample of swear/religious word bias?

Do you think this is the same for the other rappers in the sample?

Bibliography

Kastellec, Jonathan P., and Eduardo L. Leoni. 2007. “Using Graphs Instead of Tables in Political Science.” Perspectives on Politics 5 (04). https://doi.org/10.1017/s1537592707072209.